Provides a critical evaluation of data analysis techniques and current approaches to best practice evaluation techniques in the applied setting. Describes contemporary alternatives to null hypothesis testing in the context of their value for real-world professional conduct within a scientist-practitioner framework. Discusses best practices in data management, programme evaluation and scientific and applied dissemination of outcomes and utilises a combination of problem based learning applied to a project and student reflective practice.
- Designing an effective data analysis strategy: Issues and resolutions
- Introduction to the “New” statistics: effect size estimation
- Alternatives to Null Hypothesis Testing: Bayes and p-rep
- Single case designs
- Time series
- Programme evaluation
- Big data
- Good data practices
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
Learning outcomes and graduate attributes
|On completion of this unit, students should be able to:||GA1||GA2||GA3||GA4||GA5||GA6||GA7|
|1||explain and justify strategies for data analysis of typical research designs in psychology||Knowledge of a discipline|
|2||critically evaluate the use of Null Hypothesis Testing methods for data analysis and identify alternatives where more appropriate||Knowledge of a discipline|
|3||conduct appropriate analyses of data for typical research designs in psychology||Creativity|
|4||report research outcomes in an effective manner||Creativity|
- No prescribed texts.
Teaching and assessment
Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.
Commencing 2017 Commonwealth Supported only. Student contribution band: 2
Please check the international course and fee list to determine the relevant fees.